Detail výsledku
Neural Grey-Box Guitar Amplifier Modelling with Limited Data
Wright Alec
Välimäki Vesa
Schimmel Jiří, doc. Ing., Ph.D., UTKO (FEKT)
This paper combines recurrent neural networks (RNNs) with
the discretised Kirchhoff nodal analysis (DK-method) to create a
grey-box guitar amplifier model. Both the objective and subjective
results suggest that the proposed model is able to outperform
a baseline black-box RNN model in the task of modelling a guitar
amplifier, including realistically recreating the behaviour of the
amplifier equaliser circuit, whilst requiring significantly less training
data. Furthermore, we adapt the linear part of the DK-method
in a deep learning scenario to derive multiple state-space filters simultaneously.
We frequency sample the filter transfer functions in
parallel and perform frequency domain filtering to considerably reduce
the required training times compared to recursive state-space
filtering. This study shows that it is a powerful idea to separately
model the linear and nonlinear parts of a guitar amplifier using
supervised learning.
guitar amplifier modelling; grey-box modelling; recurrent neural networks; virtual analogue; discretisation; state-space model
@inproceedings{BUT184290,
author="Štěpán {Miklánek} and Alec {Wright} and Vesa {Välimäki} and Jiří {Schimmel}",
title="Neural Grey-Box Guitar Amplifier Modelling with Limited Data",
booktitle="Proceedings of the 25th International Conference on Digital Audio Effects (DAFx23)",
year="2023",
journal="Proceedings of the International Conference on Digital Audio Effects (DAFx)",
pages="8",
publisher="Aalborg University of Copenhagen",
address="Kodaň",
issn="2413-6689"
}